In a typical call center environment, routing of inbound calls is conducted with no or minimal advance knowledge of the caller's intent or reason behind the call. For example, some call centers simply route calls in a first-in-first-out (FIFO) manner to available customer service representatives (CSRs) without regard to the reason for the call or the skillset of the individual CSRs—as some CSRs may be better equipped or experienced to handle specific types of calls. In this scenario, it is not uncommon for a single call to be manually rerouted to multiple different CSRs before reaching an appropriate representative. As a result, the average time for calls on hold and for support resolution increases, adversely affecting the customer experience. In another scenario, call centers are configured to obtain just-in-time information about the caller and/or call, usually by leveraging automated systems such as interactive voice response (IVR) technology or automated attendants, and then route the call to a CSR based upon this information. In these examples, the IVR prompts are typically limited to predefined, broad options and generally do not accurately reflect the specific intent behind a customer's call. As a result, it may take several minutes for a customer to navigate an IVR menu in order to reach a CSR, and yet the CSR may still have to request or verify such information from a caller before the CSR can fully understand why the customer is calling and formulate an appropriate response.
Existing computing systems that analyze customer calls for routing to customer service agents or systems typically rely on manual input provided by the caller at the time the call is made (e.g., customer identifier, reason for call, etc.) to determine which CSR or automated computing system should receive the call. The danger is that the routing decisions made on the basis of customer-provided input may route the call to the incorrect destination (e.g., to a CSR or backend system that is not capable of resolving the issue, or to a CSR that is costlier from a resource perspective when the call is unlikely to result in addition income for the organization). In addition, these systems fail to account for prior operating income (OI) that resulted from a specific caller's previous interactions with the organization. For example, a caller with a certain user profile (e.g., asset value, account balance, demographics, etc.) may have previously called a customer service center for resolution of a problem with his or her account, but ended up adding premium services or making an additional purchase/investment stemming from the call because the call was routed to a sales-oriented CSR. As a result, it can be desirable to route that caller in the future (and other similar callers) to CSRs that can leverage income generation skills to realize additional value from the customer interaction. Existing call routing systems do not account for such income generation in making future call routing decisions, and thus these systems typically make routing decisions that may not provide an increased opportunity for additional income generation. In addition, these types of call routing systems may not be able to leverage a large corpus of historical voice call data using advanced machine learning techniques like reinforcement learning to make more accurate predictions about how to optimally route specific calls originating from specific callers. Generally, currently-available static routing systems do not take into account changing customer needs over a period of time. The needs of customers change based on a multitude of factors (e.g., life stage, financial health, etc.). A static routing system is therefore not able to optimally make call routing decisions as the underlying customer characteristics change.